Protecting assets of mutually distrustful entities during federated learning training on a remote device

A method comprising:

• receiving, at a trusted execution environment (TEE) hosted by a processor of a client platform, an encrypted machine learning (ML) model and a cryptographic message authentication code (MAC) from a model owner platform, wherein the encrypted ML model is encrypted by the model owner platform using homomorphic encryption (HE);
• successfully verifying integrity of the encrypted ML model using the cryptographic MAC and a TEE key established by the processor during remote attestation of the TEE with the model owner platform;
• responsive to successful verification of the encrypted ML model, performing, in the TEE, training of the encrypted ML model using HE computation on sensor data generated by the client platform;
• and
• sending, to the model owner platform, output of the training comprising updated model parameters of the encrypted ML model, where the output is homomorphically encrypted.

A non-transitory machine-readable storage medium having stored thereon executable computer program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving, at a trusted execution environment (TEE) hosted by a processor of a client platform, an encrypted machine learning (ML) model and a cryptographic message authentication code (MAC) from a model owner platform, wherein the encrypted ML model is encrypted by the model owner platform using homomorphic encryption (HE);
• successfully verifying integrity of the encrypted ML model using the cryptographic MAC and a TEE key established by the processor during remote attestation of the TEE with the model owner platform;
• responsive to successful verification of the encrypted ML model, performing, in the TEE, training of the encrypted ML model using HE computation on sensor data generated by the client platform;
• and
• sending, to the model owner platform, output of the training comprising updated model parameters of the encrypted ML model, where the output is homomorphically encrypted.